Brain Network Mechanisms of Instructed Learning

指导学习的脑网络机制

• 批准号: 9235846
• 负责人: Michael William Cole
• 金额: $ 41.19万
• 依托单位: RUTGERS THE STATE UNIV OF NJ NEWARK
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-13 至 2021-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9235846
• 关键词: Accounting; Auditory; Behavior; Brain; Brain region; Clinic; Cognition; Cognitive; Cognitive Therapy; Disease; Drops; Effectiveness; Feedback; Fostering; Goals; Habits; Human; Individual; Instruction; Language; Lead; Learning; Life; Link; Literature; Major Depressive Disorder; Maps; Mediating; Mental Health; Mental disorders; Methods; Motor; Network-based; Neurosciences; Pathway Analysis; Pattern; Performance; Play; Post-Traumatic Stress Disorders; Process; Psyche structure; Psychotherapy; Research; Rest; Role; Schizophrenia; Science; Sensory; Specificity; System; Testing; Time; Training; Update; Work; abstracting; base; behavior change; classical conditioning; cognitive control; cognitive function; cognitive neuroscience; cognitive training; flexibility; improved; improved outcome; injured; insight; neuroimaging; novel; relating to nervous system; skills; theories; tool; visual motor

Project Summary/Abstract: The tools of network science have enabled substantial progress in understanding the intrinsic organization of the human brain. Yet, the predominant focus on resting-state functional connectivity (FC) has become a critical barrier to progress in cognitive neuroscience, given that rest FC does not account for task-specific network changes likely essential for adaptive cognition. We offer a complementary approach – cognitive network neuroscience – which applies dynamic network analysis tools and theories to task manipulations of FC to offer insights into human cognitive function. The goal of this proposal is to utilize this network-based approach with human neuroimaging to understand how instructed learning is implemented in the human brain, from initial learning to automaticity after extensive practice. Most neuroscientific research has focused on non-instructed (e.g., exploratory or feedback-based) learning. Yet, instructed learning is highly relevant to mental health for several reasons. First, empirically supported psychotherapies (e.g., cognitive behavioral therapy) utilize the human ability for rapid instructed task learning (RITL; “rittle”) to convert instructions into cognitive strategies that improve outcomes across nearly every major mental disease. Second, RITL is impaired in a variety of mental diseases, given that RITL relies on flexible cognitive control – a general capacity supporting adaptive, goal-directed behavior important in daily life. Thus, in addition to adding difficulties to everyday life (e.g., learning new skills at work), the disruption of RITL abilities likely limits the effectiveness of psychotherapy in improving mental health. Finally, instructed learning provides an especially powerful means of experimental control over behavior change, which underlies mental health improvements even outside the context of psychotherapy. Advancing understanding of the neural basis of RITL and its transition to practiced automatized behaviors parallels the transition from instructions in the clinic to ingrained habits that can foster successful mental health change. In prior work, we built a large-scale brain network theory for how instructed learning occurs by drawing on the concept of “flexible hubs” – brain regions that coordinate goal-directed cognition (flexible control) by dynamically updating connectivity throughout the brain. The flexible hub theory strongly links the methods and theories of network science to the cognitive neuroscience of learning, and as such has the power to offer insights into the large-scale network processes underlying instructed learning. We propose to use large-scale brain network theory to understand the domain generality of flexible hubs during instructed learning (Aim 1), to determine the role of flexible hubs in the transition from novel instructed task training to practiced performance (Aims 2.1 & 2.2), and to develop RITL cognitive training that maximizes the utility of flexible hubs for performance of novel tasks (Aim 2.3). Our network-based approach to understanding instructed learning along with RITL cognitive training may lead to improved outcomes for a variety of mental disorders, given the central role instructed learning plays in empirically supported psychotherapies.

项目摘要/摘要：网络科学的工具在理解人脑的内在组织方面已经实现了重大进展。然而，鉴于REST FC并未考虑到适应性认知可能必不可少的任务网络变化，因此对静止状态功能连通性（FC）的主要关注已成为认知神经科学进展的关键障碍。我们提供了一种完整的方法 - 认知网络神经科学 - 该方法将动态网络分析工具和理论应用于FC的任务操作，以提供对人类认知功能的见解。该提案的目的是利用这种基于网络的方法和人类神经成像来了解人脑中的指导学习是如何从最初的学习到广泛练习后自动化的。大多数神经科学研究都集中在非教学（例如基于探索性或反馈）学习上。然而，由于多种原因，指导学习与心理健康高度相关。首先，紧急支持的心理治疗（例如，认知行为疗法）利用人类的快速指导任务学习能力（RITL；“ rittle”）将指导转化为认知策略，以改善几乎所有主要精神疾病的结果。其次，鉴于RITL依赖于灵活的认知控制，RITL在各种精神疾病中受到损害，这是一种一般能力，支持适应性，目标指导的行为在日常生活中很重要。除了增加日常生活（例如学习新技能）外，RITL能力的破坏可能限制了心理治疗在改善心理健康方面的有效性。最后，指导学习提供了一种特别有力的对行为改变的实验控制手段，即使在心理治疗的背景下，这也是心理健康改善的基础。促进对RITL神经基础的理解及其过渡到实践自动行为的过渡与从诊所指示到可以促进成功心理健康变化的入学习惯的过渡。在先前的工作中，我们通过借鉴“灵活枢纽”的概念来构建了一个大规模的大脑网络理论，该理论是如何进行指导学习的 - 通过动态更新整个大脑的连通性，可以通过动态更新连通性来协调目标定向的认知（灵活控制）。灵活的枢纽理论将网络科学的方法和理论与学习的认知神经科学联系起来，因此有能力能够洞悉指导学习的大规模网络过程。我们建议使用大规模的大脑网络理论来了解指导学习过程中灵活枢纽的领域通用性（AIM 1），以确定灵活中心在从新颖的指导任务培训到实践绩效的过渡中的作用（AIMS 2.1＆2.2），并开发RITL认知训练，从而开发出新颖的小型辅助型辅助性，以实现新颖的任务（目标2.3）（目标2.3）。我们基于网络的理解指导学习以及RITL认知训练的方法可能会改善各种精神障碍的结果，因为在经验支持的心理治疗中，指导学习戏剧的核心作用。